mathlib documentation

topology.metric_space.gromov_hausdorff_realized

def Gromov_Hausdorff.candidates (α : Type u) (β : Type v) [metric_space α] [compact_space α] [nonempty α] [metric_space β] [compact_space β] [nonempty β] :
set (prod_space_fun α β)

The set of functions on α ⊕ β that are candidates distances to realize the minimum of the Hausdorff distances between α and β in a coupling

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def Gromov_Hausdorff.candidates_b_of_candidates {α : Type u} {β : Type v} [metric_space α] [compact_space α] [nonempty α] [metric_space β] [compact_space β] [nonempty β] (f : prod_space_fun α β) :
f Gromov_Hausdorff.candidates α βCb α β

candidates give rise to elements of bounded_continuous_functions

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theorem Gromov_Hausdorff.candidates_b_of_candidates_mem {α : Type u} {β : Type v} [metric_space α] [compact_space α] [nonempty α] [metric_space β] [compact_space β] [nonempty β] (f : prod_space_fun α β) (fA : f Gromov_Hausdorff.candidates α β) :

def Gromov_Hausdorff.candidates_b_dist (α : Type u) (β : Type v) [metric_space α] [compact_space α] [inhabited α] [metric_space β] [compact_space β] [inhabited β] :
Cb α β

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def Gromov_Hausdorff.HD {α : Type u} {β : Type v} [metric_space α] [compact_space α] [nonempty α] [metric_space β] [compact_space β] [nonempty β] :
Cb α β

We will then choose the candidate minimizing the Hausdorff distance. Except that we are not in a metric space setting, so we need to define our custom version of Hausdorff distance, called HD, and prove its basic properties.

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theorem Gromov_Hausdorff.HD_below_aux1 {α : Type u} {β : Type v} [metric_space α] [compact_space α] [nonempty α] [metric_space β] [compact_space β] [nonempty β] {f : Cb α β} (C : ) {x : α} :
bdd_below (set.range (λ (y : β), f (sum.inl x, sum.inr y) + C))

theorem Gromov_Hausdorff.HD_below_aux2 {α : Type u} {β : Type v} [metric_space α] [compact_space α] [nonempty α] [metric_space β] [compact_space β] [nonempty β] {f : Cb α β} (C : ) {y : β} :
bdd_below (set.range (λ (x : α), f (sum.inl x, sum.inr y) + C))

Explicit bound on HD (dist). This means that when looking for minimizers it will be sufficient to look for functions with HD(f) bounded by this bound.

With the optimal candidate, construct a premetric space structure on α ⊕ β, on which the predistance is given by the candidate. Then, we will identify points at 0 predistance to obtain a genuine metric space

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def Gromov_Hausdorff.optimal_GH_coupling (α : Type u) (β : Type v) [metric_space α] [compact_space α] [nonempty α] [metric_space β] [compact_space β] [nonempty β] :
Type (max u v)

A metric space which realizes the optimal coupling between α and β

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Injection of α in the optimal coupling between α and β

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The injection of α in the optimal coupling between α and β is an isometry.

Injection of β in the optimal coupling between α and β

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The injection of β in the optimal coupling between α and β is an isometry.

@[instance]

The optimal coupling between two compact spaces α and β is still a compact space

For any candidate f, HD(f) is larger than or equal to the Hausdorff distance in the optimal coupling. This follows from the fact that HD of the optimal candidate is exactly the Hausdorff distance in the optimal coupling, although we only prove here the inequality we need.